An Artificial Intelligence Based Optimal Technique for Coordinating Multiple Setting Group Type Overcurrent Relays Considering Network with Renewable-Resources
Keywords:
Artificial Intelligence, Coordination time interval, K-Means, Distributed Generator, Renewable Energy, DOCR, Genetic Algorithm, DIgSILENTAbstract
Integrating renewable based resources with the network changes the power flow and makes relay coordination more complex and challenging. Looking at the supportive environment for renewable based distributed generation on the grid, the penetration of RE-based distributed generation continuously increases. Relay coordination optimization challenges are complex problems that involve several objectives and constraints to DGs. The aim is to minimize the operation time of each relay while ensuring that all relevant constraints are met. The coordination constraints consist of fulfilling the coordination time interval (CTI) and adhering to the time requirements for relay operations. In order to address coordination optimization problems, decision-making variables are established to minimize the objective function while ensuring that the essential constraints are satisfied. Most available research focuses on defining relay coordination for fixed network topologies. However, relay coordination varies in practice due to many factors, such as maintenance and element failures. Modern overcurrent relays can store multiple relay settings, but the number of settings they can store is limited compared to the vast network configurations. To address this, a suitable clustering technique like K-Means can be employed to group different network topologies using an appropriate clustering index. The target function and the constraints are both time-dependent, so the standard index fault current deviation is not used as the clustering index. Instead, time-dependent features like the average relay operational time are used. IEEE-14 bus system along with wind and solar type DG has been used to address the optimization problem.
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